Radio resource control procedures for machine learning

ABSTRACT

An example method, apparatus, and computer-readable storage medium are provided for radio resource control (RRC) procedures for machine learning (ML). In an example implementation, the method may include receiving, by a user equipment (UE), machine learning (ML) configuration from a network node; collecting, by the user equipment (UE), machine learning (ML) data based at least on the machine learning (ML) configuration received from the network node, the machine learning (ML) data being collected from one or more layers of the user equipment (UE) in a coordinated manner; and transmitting, by the user equipment (UE), the collected machine learning (ML) data to the network node. In another example implementation, the method may include transmitting, by a network node, machine learning (ML) configuration to a user equipment (UE); and receiving, by the network node, machine learning (ML) data from the user equipment (UE), the machine learning (ML) data received in response to the machine learning (ML) configuration transmitted to the user equipment (UE).

TECHNICAL FIELD

This description relates to wireless communications, and in particular,to data collection for machine learning (ML).

BACKGROUND

A communication system may be a facility that enables communicationbetween two or more nodes or devices, such as fixed or mobilecommunication devices. Signals can be carried on wired or wirelesscarriers.

An example of a cellular communication system is an architecture that isbeing standardized by the 3rd Generation Partnership Project (3GPP). Arecent development in this field is often referred to as the long-termevolution (LTE) of the Universal Mobile Telecommunications System (UMTS)radio-access technology. E-UTRA (evolved UMTS Terrestrial Radio Access)is the air interface of 3GPP's Long Term Evolution (LTE) upgrade pathfor mobile networks. In LTE, base stations or access points (APs), whichare referred to as enhanced Node AP or Evolved Node B (eNBs), providewireless access within a coverage area or cell. In LTE, mobile devices,or mobile stations are referred to as user equipments (UE). LTE hasincluded a number of improvements or developments.

5G New Radio (NR) is part of a continued mobile broadband evolutionprocess to meet the requirements of 5G, similar to earlier evolution of3G & 4G wireless networks. In addition, 5G is also targeted at the newemerging use cases in addition to mobile broadband. A goal of 5G is toprovide significant improvement in wireless performance, which mayinclude new levels of data rate, latency, reliability, and security. 5GNR may also scale to efficiently connect the massive Internet of Things(IoT), and may offer new types of mission-critical services.Ultra-reliable and low-latency communications (URLLC) devices mayrequire high reliability and very low latency.

SUMMARY

An example method, apparatus, and computer-readable storage medium areprovided for radio resource control (RRC) procedures for machinelearning (ML).

In an example implementation, the method may include receiving, by auser equipment (UE), machine learning (ML) configuration from a networknode; collecting, by the user equipment (UE), machine learning (ML) databased at least on the machine learning (ML) configuration received fromthe network node, the machine learning (ML) data being collected fromone or more layers of the user equipment (UE) in a coordinated manner;and transmitting, by the user equipment (UE), the collected machinelearning (ML) data to the network node.

In another example implementation, the method may include transmitting,by a network node, machine learning (ML) configuration to a userequipment (UE); and receiving, by the network node, machine learning(ML) data from the user equipment (UE), the machine learning (ML) datareceived in response to the machine learning (ML) configurationtransmitted to the user equipment (UE).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a wireless network according to an exampleimplementation.

FIG. 2 is a message flow diagram illustrating procedures for machinelearning (ML) at a user equipment (UE), according to an exampleimplementation.

FIG. 3 is a block diagram illustrating ML functions in control plane(CP) protocol stack, according to an example implementation.

FIG. 4 is a block diagram illustrating ML functions in control plane(CP) protocol stack, according to an additional example implementation.

FIG. 5 is a block diagram illustrating ML control plane (CP) protocolstack, according to another additional example implementation.

FIG. 6 is a flow chart illustrating data collection for machine learningat a user equipment (UE), according to at least one exampleimplementation.

FIG. 7 is a flow chart illustrating data collection for machine learningby a network node, according to at least one example implementation.

FIG. 8 is a block diagram of a node or wireless station (e.g., basestation/access point or mobile station/user device/UE), according to anexample implementation.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of a wireless network 130 according to anexample implementation. In the wireless network 130 of FIG. 1, userdevices (UDs) 131, 132, 133 and 135, which may also be referred to asmobile stations (MSs) or user equipment (UEs), may be connected (and incommunication) with a base station (BS) 134, which may also be referredto as an access point (AP), an enhanced Node B (eNB), a next generationNode B (gNB), or a network node. At least part of the functionalities ofan access point (AP), base station (BS), or eNB/gNB may also be carriedout by any node, server or host which may be operably coupled to atransceiver, such as a remote radio head. BS (or AP) 134 provideswireless coverage within a cell 136, including to user devices 131, 132,133 and 135. Although only four user devices are shown as beingconnected or attached to BS 134, any number of user devices may beprovided. BS 134 is also connected to a core network 150 via a S1interface 151. This is merely one simple example of a wireless network,and others may be used.

A user device (user terminal, user equipment (UE)) may refer to aportable computing device that includes wireless mobile communicationdevices operating with or without a subscriber identification module(SIM), including, but not limited to, the following types of devices: amobile station (MS), a mobile phone, a cell phone, a smartphone, apersonal digital assistant (PDA), a handset, a device using a wirelessmodem (alarm or measurement device, etc.), a laptop and/or touch screencomputer, a tablet, a phablet, a game console, a notebook, and amultimedia device, as examples, or any other wireless device. It shouldbe appreciated that a user device may also be a nearly exclusive uplinkonly device, of which an example is a camera or video camera loadingimages or video clips to a network.

In LTE (as an example), core network 150 may be referred to as EvolvedPacket Core (EPC), which may include a mobility management entity (MME)which may handle or assist with mobility/handover of user devicesbetween BSs, one or more gateways that may forward data and controlsignals between the BSs and packet data networks or the Internet, andother control functions or blocks.

In addition, by way of illustrative example, the various exampleimplementations or techniques described herein may be applied to varioustypes of user devices or data service types, or may apply to userdevices that may have multiple applications running thereon that may beof different data service types. New Radio (5G) development may supporta number of different applications or a number of different data servicetypes, such as for example: machine type communications (MTC), enhancedmachine type communication (eMTC), Internet of Things (IoT), and/ornarrowband IoT user devices, enhanced mobile broadband (eMBB), andultra-reliable and low-latency communications (URLLC).

IoT may refer to an ever-growing group of objects that may have Internetor network connectivity, so that these objects may send information toand receive information from other network devices. For example, manysensor type applications or devices may monitor a physical condition ora status, and may send a report to a server or other network device,e.g., when an event occurs. Machine Type Communications (MTC or machineto machine communications) may, for example, be characterized by fullyautomatic data generation, exchange, processing and actuation amongintelligent machines, with or without intervention of humans. Enhancedmobile broadband (eMBB) may support much higher data rates thancurrently available in LTE.

Ultra-reliable and low-latency communications (URLLC) is a new dataservice type, or new usage scenario, which may be supported for NewRadio (5G) systems. This enables emerging new applications and services,such as industrial automations, autonomous driving, vehicular safety,e-health services, and so on. 3GPP targets in providing up to e.g., 1 msU-Plane (user/data plane) latency connectivity with 1-1e-5 reliability,by way of an illustrative example. Thus, for example, URLLC userdevices/UEs may require a significantly lower block error rate thanother types of user devices/UEs as well as low latency. Thus, forexample, a URLLC UE (or URLLC application on a UE) may require muchshorter latency, as compared to an eMBB UE (or an eMBB applicationrunning on a UE).

The various example implementations may be applied to a wide variety ofwireless technologies or wireless networks, such as LTE, LTE-A, 5G, IoT,MTC, eMTC, eMBB, URLLC, etc., or any other wireless network or wirelesstechnology. These example networks, technologies or data service typesare provided only as illustrative examples. Multiple Input, MultipleOutput (MIMO) may refer to a technique for increasing the capacity of aradio link using multiple transmit and receive antennas to exploitmultipath propagation. MIMO may include the use of multiple antennas atthe transmitter and/or the receiver. MIMO may include amulti-dimensional approach that transmits and receives two or moreunique data streams through one radio channel. For example, MIMO mayrefer to a technique for sending and receiving more than one data signalsimultaneously over the same radio channel by exploiting multipathpropagation. According to an illustrative example, multi-user multipleinput, multiple output (multi-user MIMO, or MU-MIMO) enhances MIMOtechnology by allowing a base station (BS) or other wireless node tosimultaneously transmit or receive multiple streams to different userdevices or UEs, which may include simultaneously transmitting a firststream to a first UE, and a second stream to a second UE, via a same (orcommon or shared) set of physical resource blocks (PRBs) (e.g., whereeach PRB may include a set of time-frequency resources).

Also, a BS may use precoding to transmit data to a UE (based on aprecoder matrix or precoder vector for the UE). For example, a UE mayreceive reference signals or pilot signals, and may determine aquantized version of a DL channel estimate, and then provide the BS withan indication of the quantized DL channel estimate. The BS may determinea precoder matrix based on the quantized channel estimate, where theprecoder matrix may be used to focus or direct transmitted signal energyin the best channel direction for the UE. Also, each UE may use adecoder matrix may be determined, e.g., where the UE may receivereference signals from the BS, determine a channel estimate of the DLchannel, and then determine a decoder matrix for the DL channel based onthe DL channel estimate. For example, a precoder matrix may indicateantenna weights (e.g., an amplitude/gain and phase for each weight) tobe applied to an antenna array of a transmitting wireless device.Likewise, a decoder matrix may indicate antenna weights (e.g., anamplitude/gain and phase for each weight) to be applied to an antennaarray of a receiving wireless device. This applies to UL as well when aUE is transmitting data to a BS.

For example, according to an example aspect, a receiving wireless userdevice may determine a precoder matrix using Interference RejectionCombining (IRC) in which the user device may receive reference signals(or other signals) from a number of BSs (e.g., and may measure a signalstrength, signal power, or other signal parameter for a signal receivedfrom each BS), and may generate a decoder matrix that may suppress orreduce signals from one or more interferers (or interfering cells orBSs), e.g., by providing a null (or very low antenna gain) in thedirection of the interfering signal, in order to increase a signal-tointerference plus noise ratio (SINR) of a desired signal. In order toreduce the overall interference from a number of different interferers,a receiver may use, for example, a Linear Minimum Mean Square ErrorInterference Rejection Combining (LMMSE-IRC) receiver to determine adecoding matrix. The IRC receiver and LMMSE-IRC receiver are merelyexamples, and other types of receivers or techniques may be used todetermine a decoder matrix. After the decoder matrix has beendetermined, the receiving UE/user device may apply antenna weights(e.g., each antenna weight including amplitude and phase) to a pluralityof antennas at the receiving UE or device based on the decoder matrix.Similarly, a precoder matrix may include antenna weights that may beapplied to antennas of a transmitting wireless device or node. Thisapplies to a receiving BS as well.

The increased complexity of 5G wireless networks may provide networkoperators with an unprecedented opportunity to optimize networkperformance in real-time (RT) or near-RT to extract the full benefitsthat the new(er) technologies offer. Algorithms based on ML may become afundamental tool for network optimization and/or automation and startingto appear in standardization organizations. O-RAN Alliance is one suchorganization where ML models are being discussed in the context of radioaccess network (RAN) architecture.

O-RAN alliance has defined requirements for monitoring service levelagreements (SLAs). However, the alliance has not defined how to performtheir monitoring. Although, the monitoring of SLAs may be generallyunderstood/assumed to be based on collecting performance measurement(PM) counters, the monitoring data is not collected in RT or near-RT. Atthe same time, ML is emerging in 3GPP with network operators settingtargets for standardized collection methods that are efficient.Therefore, ML has the potential to become powerful tool by makingpredictions or suggestions based on large amounts of data (e.g., alsoreferred to as Big Data) that may be collected at UEs and/or gNBs, or acombination thereof.

However, there are several problems associated with data collection forML algorithms from the end users in the field (e.g., UEs) as thecollected data has to meet certain requirements to be useful for ML. Forinstance, radio resource control (RRC) procedures may enable datacollection (e.g., parameters, reports, etc.) by a radio access network(RAN) entity from a user equipment (UE). However, the data may come withdifferent granularity, in different times for different purposes withdifferent reports and procedures. That is, there is no coordination inthe data collection process for the collected data to be useful for ML.

The present disclosure addresses the above described problems associatedwith data collection for ML algorithms such that the collected data canbe used efficiently for ML. The present disclosure describes acoordinated method for collecting data at a UE for ML.

In one example implementation, the method may be initiated at a RANnetwork function (NF) that may have the role of a ML host (e.g., wherethe ML algorithm is executed) with a direct interface to a UE and maycoordinate inputs from other ML hosts (e.g., the algorithm may be madeof a ML pipeline, a chain of ML algorithms, etc.). The inventive stepmay include the RAN NF recognizing the need to collect data for MLpurposes from the UE and triggering the RAN NF to query the UE tocollect data in a manner and structure that can be used by the MLalgorithm. The UE responds to the query by collecting and processingrequired data as a separate record (e.g., storage, reporting, etc.) forML. In some implementations, the proposed method may provide: i) RANcapability to determine the need to involve users for collecting datafor ML Learning; ii) new signaling specific for ML, realized either by aseparate ML dedicated protocol stack or by RRC radio interface with newprocedures for ML; and/or iii) UE capability to act on the signaling(determining which data needs to be delivered and provisioning of thedata for ML).

In an example implementation, the disclosure describes a method that mayinclude receiving, by a user equipment (UE), machine learning (ML)configuration from a network node; collecting, by the user equipment(UE), machine learning (ML) data based at least on the machine learning(ML) configuration received from the network node, the machine learning(ML) data being collected from one or more layers of the user equipment(UE) in a coordinated manner; and transmitting, by the user equipment(UE), the collected machine learning (ML) data to the network node.

In another example implementation, the disclosure describes as a methodthat include may include transmitting, by a network node, machinelearning (ML) configuration to a user equipment (UE); and receiving, bythe network node, machine learning (ML) data from the user equipment(UE), the machine learning (ML) data received in response to the machinelearning (ML) configuration transmitted to the user equipment (UE).

FIG. 2 is a message flow diagram 200 illustrating procedures for machinelearning (ML) at a user equipment (UE), according to an exampleimplementation.

In some implementations, for example, FIG. 2 illustrates a UE (e.g., UE202) and a network node (e.g., a gNB 204). Although, only UE 202 and gNB204 are illustrated in FIG. 2, in an example implementation, the presentdisclosure may be applied to a plurality of UEs located in the coveragearea of gNB 204. In another example implementation, the presentdisclosure may be applied to a plurality of gNBs when collecting data atcluster/network level for ML. In another additional exampleimplementation, the present disclosure may be applied to a plurality ofUEs and/or a plurality of UEs for ML.

In an example implementation, ML procedures at the gNB may be triggeredin response to a message received at the gNB, for example, from anothernetwork entity, e.g., core network (CN) entity, radio intelligentcontroller (RIC), or an Operations, Administration and Management (OAM)entity.

At 212, gNB 204 may send UECapabilityEnquiry message, an RRC message, toUE 202. The UECapabilitylnformation message requests UE 202 to indicatethe capabilities of the UE to the gNB. In some implementations, forexample, gNB 204 may send the UECapabilityEnquiry message to UE 202 uponreceiving of a trigger by the gNB.

At 214, UE 202 may transmit UECapabilitylnformation message to gNB 204in response to receiving the UECapabilityEnquiry message from the gNB at212. The UECapabilitylnformation message is an RRC message whichindicates capabilities of the UE to the gNB. In some implementations,for example, the UECapabilitylnformation message may include aninformation element (IE) indicating ML capabilities of the UE. In someimplementations, for example, the UECapabilitylnformation message mayinclude an information element (IE) indicating ML capabilities of the UEin response to a selective query in the UECapabilityEnquiry message(e.g., on whether the ML capabilities are supported).

At 216, gNB 204 may generate an ML configuration for the UE. In someimplementations, for example, gNB 204 may generate ML configuration forUE 202 based at least on the capabilities indicated in theUECapabilitylnformation message received from the UE and/or informationreceived in the trigger.

At 218, gNB 204 may transmit the generated ML configuration to UE 202.In some implementations, for example, the ML configuration may include aML command which indicates the ML data to be collected at the UE. In anexample implementation, the ML command may indicate to the UE to collectan error metric at one or more layers of the UE. In another exampleimplementation, the ML command may indicate to the UE to collect packetsexceeding a specific payload size at one or more layers of the UE.

In some implementations, for example, the ML configuration may beupdated by the gNB by sending new ML configuration to the UE. In somemore implementations, for example, ML configuration may be initiallysent to the UE such that the ML configuration may beactivated/deactivated by the gNB as needed. In some moreimplementations, for example, the ML configuration may be sent to the UEwith an expiration time. That is, the ML configuration expires after afixed amount of time (e.g., 3 hours). In some more implementations, theML configuration may be sent to the UE with a validity duration (forexample, HH1:MM1:SS1—HH2:MM2:SS2).

In some implementations, for example, the ML configuration may be sentto the UE as a separate RRC message, included as part of RRCconfiguration procedure or RRC data query. In a scenario where the MLconfiguration is sent as part of RRC configuration procedure or RRC dataquery, the RRC message may contain an attribute (or a parameter, afield, etc.) that may indicate the need for collecting ML data.

In one example implementation, the ML configuration may include a fixedpayload, e.g., at least a bit/a flag. In another example implementation,the ML configuration may contain payload that may be changed/extendedwith explicit indication on what data/reports are being requested. Insome implementations, for example, the parameters of the MLconfiguration may indicate whether the request is for RT or non-RT data.The gNB may use this information to decide how and from which UEs torequest the ML data (e.g., whether raw data is required to be collectedby certain UEs or some averages are required). In some implementations,this may also depend on the UE capabilities and the indicated memoryreserved for ML.

In some implementation, the UECapabilitylnformation IE may indicate UEcapabilities related to ML and indicates the amount of memory the UE canreserve for ML. The gNB, based on the indicated UE capabilities, maydetermine whether the UE can be selected (or another UE should beselected for ML). This determination, in some implementations, may bebased 3GPP TS 38.331. As memory and processing capabilities associatedwith ML may play a role, the gNB may consider memory available for MLwhen picking a UE for ML and may pick another UE if the other UE hasbetter ML capabilities (e.g., higher memory and/or processing power).

At 220, UE 202 may save (e.g., store) the received ML configuration atthe UE.

At 222, UE 202 may collect the ML data based at least on the saved MLconfiguration and send the collected ML data to gNB 204. In someimplementations, for example, an ML function at the UE (referred to asUE ML Function) may coordinate the collection and/or transmission of theML data to the gNB. Similarly, a RAN ML Function at the gNB 204 maycoordinate the ML operations at the gNB.

In an example implementation, as described above, the ML command mayhave indicated to collect an error metric at one or more layers of theUE. As this may occur at different times at different layers, the MLfunction or ML at the UE (as described in detail in reference to FIGS.3-5) may coordinate the collection of data from the different layers andreport it to the gNB, once a reporting trigger is met.

The ML Functions at UE and gNB may be implemented in several ways. A fewexample implementations are described in detail below in reference toFIGS. 3-5. These are for illustration and explanation purposes only andshould not considered or interpreted as limitations.

At 224, upon receipt of the ML data received from UE 202, in someimplementations, for example, gNB 204 may perform actions based at leaston the ML data received from the UE. In an example implementation, theaction may include changing values of one or parameters at the UE fornetwork optimization.

In some implementations, for example, the RRC-placed ML specific memoryand procedures may require the UE to recognize and mark the data thatare required for ML report. For example, if RSRP measurements that havebeen performed for Radio Resource Management (RRM) satisfy the conditionfor ML report, the RSRP measurements are included in the ML report andsend using ML specific procedures. In addition, the RRC-placed MLspecific memory may require cross-layer interfacing and actions tocollect ML specific data from other protocol stacks (e.g., if they wereML configuration command). In some implementations, for example, thememory may be a separate entry and place (e.g., in a separate protocolstack).

Although the present disclosure describes the ML techniques using radioresource control (RRC) procedures, which may be built on the existingstandard protocol stacks, it should be noted that the present disclosureis not limited to Uu protocol (air interface between UE an gNB). In someimplementations, the techniques described in the present disclosure maybe implemented using ML-specific signaling (or ML-specific protocol).

Thus, the proposed disclosure describes efficient procedures forcollecting data for ML.

FIG. 3 is a block diagram 300 illustrating ML functions in control plane(CP) protocol stack, according to an example implementation.

In some implementations, for example, upon receiving the MLconfiguration from gNB 204, UE 202 may coordinate collection of ML datafrom one or more layers (RRC 321, PDCP 322, RLC 323, MAC 324, and/or PHY325) at the UE. The UE coordinates ML data collection based at least onthe ML command in the ML configuration. In addition, as described above,in some implementations, for example, the ML configuration may be sentto the UE as a separate RRC message, included as part of RRCconfiguration procedure or RRC data query.

In an example implementation, RAN ML Function 304 at RRC layer of gNB204 may transmit the ML command via RRC signaling to UE 202. UE MLFunction 302 located at the RRC layer of the UE may interface with RANML Function 304 to receive the ML configuration and/or ML command. Inaddition, UE ML Function 302 coordinates collection of ML data from oneor more layers of the UE and/or transmits the collected ML data to thegNB. In some implementations, UE ML Function 302 and RAN ML Function 304may be considered as ML entities at UE 202 and gNB 204, respectively.

FIG. 4 is a block diagram 400 illustrating ML functions in control plane(CP) protocol stack, according to an additional example implementation.

In some implementations, for example, upon receiving the MLconfiguration from the gNB, UE 202 may coordinate collection of ML datafrom one or more layers (RRC 421, PDCP 422, RLC 423, MAC 424, and/or PHY425) at the UE via direct interfaces from the RRC layer to the otherlayers (e.g., PDCP, RLC, MAC, etc.). The UE coordinates ML datacollection based at least on the ML command in the ML configuration.

In an example implementation, RAN ML Function 404 (which be same/similarto RAN ML Function 304 of FIG. 3) at RRC layer of gNB 204 may transmitthe ML command via RRC signaling to UE 202. UE ML Function 402 locatedat the RRC layer of the UE may interface with RAN ML Function 404 toreceive the ML configuration and/or ML command. In addition, UE MLFunction 402 coordinates collection of ML data from RRC 521 and/or otherlayers (e.g., PDCP 522, RLC 523, MAC 524, and/or PHY 525) of the UE viadirect interfaces (440 of FIG. 4) to these layers and/or transmits thecollected ML data to the gNB. In some implementations, UE ML Function402 and RAN ML Function 404 may be considered as ML entities at UE 202and gNB 204, respectively.

FIG. 5 is a block diagram 500 illustrating ML control plane (CP)protocol stack, according to another additional example implementation.

In some implementations, the control plane protocol stack between UE 202and gNB 202 may include an ML layer 520 at UE 202 and an ML layer 530 atgNB 202. In an example implementation, the UE may receive the MLconfiguration from the gNB via ML layer 520 (from ML layer 530 at thegNB). ML layer 520 may collect data from one or more layers at the UEand transmits the collected ML data to the gNB (e.g., ML layer 530). TheUE coordinates ML data collection based at least on the ML command inthe ML configuration. In some implementations, the trigger for ML may betriggered at a network entity ML Function 550 (e.g., CN, OAM, RIC,etc.). In some implementations, ML layer 520 and RAN ML layer 530 may beconsidered as ML entities at UE 202 and gNB 204, respectively.

FIG. 6 is a flow chart 600 illustrating data collection for machinelearning at a user equipment (UE), according to at least one exampleimplementation.

At block 610, a UE (e.g., UE 202) may receive ML configuration from anetwork node (e.g., gNB 204). In an example implementation, UE 402 mayreceive configuration information from gNB 410. The configurationinformation, for example, may include a listing of cells the UE may usefor configuring as secondary cells for DC/CA.

At block 620, the UE may collect ML data. In an example implementation,the ML data collected at UE 202 may be based at least on the MLconfiguration received from network node 204.

In some implementations, UE 202 may collect ML data from one or morelayers at the UE. In one example implementation, UE 202 may collect MLdata from RRC and PDCP layers. In another example implementation, UE 202may collect ML data from PDCP and RLC layers. In another additionalexample implementation, UE 202 may collect ML data from RRC and MAClayers. It should be noted that these are just some examples and shouldnot be considered as limitations.

At block 630, the UE may transmit the collected ML data to the networknode. In an example implementation, UE 202 may transmit the ML datacollected at the UE to the network node.

In some implementations, UE 202 may coordinate the collection of ML datafrom one or more layers at the UE and transmits the collected ML data tothe network node.

Additional example implementations are described herein.

Example 1. A method of communications, comprising: receiving, by a userequipment (UE), machine learning (ML) configuration from a network node;collecting, by the user equipment (UE), machine learning (ML) data basedat least on the machine learning (ML) configuration received from thenetwork node, the machine learning (ML) data being collected from one ormore layers of the user equipment (UE) in a coordinated manner; andtransmitting, by the user equipment (UE), the collected machine learning(ML) data to the network node.

Example 2. The method of Example 1, further comprising: receiving, bythe user equipment (UE), a UECapabilityEnquiry message from the networknode; and transmitting, by the user equipment (UE), aUECapabilitylnformation message to the network node, theUECapabilitylnformation message is transmitted to the network node inresponse to the receiving of the UECapabilityEnquiry message from thenetwork node, wherein the machine learning (ML) configuration isreceived from the network node based at least on machine learning (ML)capabilities of the UE indicated to the network node in theUECapabilitylnformation message.

Example 3. The method of any combination of Examples 1-2, wherein theuser equipment (UE) coordinates the collecting of the machine learning(ML) data at the user equipment (UE), the collecting based at least on amachine learning (ML) command in the machine learning (ML)configuration.

Example 4. The method of any combination of Examples 1-3, wherein themachine learning (ML) configuration includes a machine learning (ML)command that indicates the machine learning (ML) data to be collected inthe coordinated manner at the user equipment (UE).

Example 5. The method of any combination of Examples 1-4, wherein themachine learning (ML) command is received via radio resource control(RRC) signaling.

Example 6. The method of any combination of Examples 1-5, wherein amachine learning (ML) entity at a radio resource control (RRC) layer ofthe user equipment (UE) collects the machine learning (ML) datagenerated at the radio resource control (RRC) layer of the userequipment (UE).

Example 7. The method of any combination of Examples 1-6, wherein themachine learning (ML) entity at the radio resource control (RRC) layerof the user equipment (UE) manages the transmitting of the datacollected at the radio resource control (RRC) layer of the userequipment (UE) to the network node.

Example 8. The method of any combination of Examples 1-7, wherein amachine learning (ML) entity at a radio resource control (RRC) layer ofthe user equipment (UE) collects data generated at the radio resourcecontrol (RRC) layer of the user equipment (UE) and one or more otherlayers of the user equipment (UE).

Example 9. The method of any combination of Examples 1-8, wherein theone or more other layers include: a packet data convergence protocol(PDCP) layer; a radio link control (RLC) layer; a media access control(MAC) layer; and a physical (PHY) layer.

Example 10. The method of any combination of Examples 1-9, wherein themachine learning (ML) entity at the radio resource control (RRC) layerof the user equipment (UE) manages the transmitting of the datacollected at the radio resource control (RRC) layer and the one or moreother layers of the user equipment (UE).

Example 11. The method of any combination of Examples 1-10, wherein themachine learning (ML) command is received via machine learning (ML)layer signaling.

Example 12. The method of any combination of Examples 1-11, wherein amachine learning (ML) layer of the user equipment (UE) collects themachine learning (ML) data generated at one or more other layers of theuser equipment (UE).

Example 13. The method of any combination of Examples 1-12, wherein theone or more other layers include: a radio resource control (RRC) layer;a packet data convergence protocol (PDCP) layer; a radio link control(RLC) layer; a media access control (MAC) layer; and a physical (PHY)layer.

Example 14. The method of any combination of Examples 1-13, wherein themachine learning (ML) entity at the machine learning (ML) layer of theuser equipment (UE) manages the transmitting of the machine learning(ML) data collected at the machine learning (ML) layer to the networknode.

Example 15. The method of any combination of Examples 1-14, wherein thenetwork node is a gNB.

Example 16. An apparatus comprising at least one processor and at leastone memory including computer instructions, when executed by the atleast one processor, cause the apparatus to perform a method of any ofExamples 1-15.

Example 17. An apparatus comprising means for performing a method of anyof Examples 1-15.

Example 18. A non-transitory computer-readable storage medium havingstored thereon computer executable program code which, when executed ona computer system, causes the computer system to perform the steps ofany of Examples 1-15.

FIG. 7 is a flow chart 700 illustrating data collection for machinelearning by a network node, according to at least one exampleimplementation.

At block 710, a network node (e.g., gNB 204) may transmit MLconfiguration to a user equipment (e.g., UE 202). In someimplementations, for example, the ML configuration is generated by gNB204 in response to a trigger received by the gNB. The trigger may beassociated with initiating ML procedures at gNB 204 and/or UE 202.

In some implementations, for example, the ML configuration may include aML command which may indicate to the UE the ML data to be collected atthe UE for transmissions to the gNB.

At block 720, gNB 204 may receive ML data from the UE. In an exampleimplementation, the ML data may be received by gNB 204 in response tothe transmission of the ML configuration to the UE.

In some implementations, for example, the network node, upon receivingof a trigger, may transmit a UECapabilityEnquiry Message to the UE. Uponthe transmission of the UECapabilityEnquiry Message to the UE, gNB 204may receive an UECapabilityInformation message from the user equipment(UE). The UECapabilitylnformation message may indicate the MLcapabilities of the user equipment (UE) as different UEs may havedifferent ML capabilities. In an example implementation, the MLcapabilities of the UE indicated in the UECapabilitylnformation messagereceived from the UE may be used by gNB 204 to generate the MLconfiguration transmitted to the UE.

In some implementations, gNB 204, the gNB may process the received MLdata and perform one or more actions. In some implementation, gNB 204may forward the received ML data to another entity, e.g., core networkentity, for further processing.

Additional example implementations are described herein.

Example 19. A method of communications, comprising: transmitting, by anetwork node, machine learning (ML) configuration to a user equipment(UE); and receiving, by the network node, machine learning (ML) datafrom the user equipment (UE), the machine learning (ML) data received inresponse to the machine learning (ML) configuration transmitted to theuser equipment (UE).

Example 20. The method of Example 19, further comprising: transmitting,by the network node, a UECapabilityEnquiry message to the user equipment(UE); and receiving, by the network node, an UECapabilitylnformationmessage from the user equipment (UE), the UECapabilitylnformationmessage is received by the network node in response to the transmittingof the UECapabilityEnquiry message to the user equipment (UE), whereinthe UECapabilitylnformation message indicates machine learningcapabilities of the user equipment (UE).

Example 21. The method of any combination of Examples 19-20, furthercomprising: generating the machine learning (ML) configuration based atleast on the machine learning capabilities of the user equipment (UE)indicated by the user equipment (UE) in the UECapabilitylnformationmessage.

Example 22. The method of any combination of Examples 19-21, furthercomprising: performing one or more actions based on at least on machinelearning (ML) data received from the user equipment (UE).

Example 23. The method of any combination of Examples 19-22, whereinmachine learning (ML) configuration is generated in response to atrigger to initiate machine learning (ML) procedures at the network nodeand/or the user equipment (UE).

Example 24. The method of any combination of Examples 19-23, whereinmachine learning (ML) configuration includes a machine learning (ML)command that indicates to the user equipment (UE) the machine learning(ML) data to be collected at the user equipment (UE).

Example 25. The method of any combination of Examples 19-24, wherein thenetwork node is a gNB.

Example 26. An apparatus comprising at least one processor and at leastone memory including computer instructions, when executed by the atleast one processor, cause the apparatus to perform a method of any ofExamples 19-25.

Example 27. An apparatus comprising means for performing a method of anyof Examples 19-25.

Example 28. A non-transitory computer-readable storage medium havingstored thereon computer executable program code which, when executed ona computer system, causes the computer system to perform the steps ofany of Examples 19-25.

FIG. 8 is a block diagram 800 of a wireless station (e.g., userequipment (UE)/user device or AP/gNB/MgNB/SgNB) according to an exampleimplementation. The wireless station 800 may include, for example, oneor more RF (radio frequency) or wireless transceivers 802A, 802B, whereeach wireless transceiver includes a transmitter to transmit signals anda receiver to receive signals. The wireless station also includes aprocessor or control unit/entity (controller) 804/808 to executeinstructions or software and control transmission and receptions ofsignals, and a memory 806 to store data and/or instructions.

Processor 804 may also make decisions or determinations, generateframes, packets or messages for transmission, decode received frames ormessages for further processing, and other tasks or functions describedherein. Processor 804, which may be a baseband processor, for example,may generate messages, packets, frames or other signals for transmissionvia wireless transceiver 802 (802A or 802B). Processor 804 may controltransmission of signals or messages over a wireless network, and maycontrol the reception of signals or messages, etc., via a wirelessnetwork (e.g., after being down-converted by wireless transceiver 802,for example). Processor 804 may be programmable and capable of executingsoftware or other instructions stored in memory or on other computermedia to perform the various tasks and functions described above, suchas one or more of the tasks or methods described above. Processor 804may be (or may include), for example, hardware, programmable logic, aprogrammable processor that executes software or firmware, and/or anycombination of these. Using other terminology, processor 804 andtransceiver 802 together may be considered as a wirelesstransmitter/receiver system, for example.

In addition, referring to FIG. 8, a controller (or processor) 808 mayexecute software and instructions, and may provide overall control forthe station 800, and may provide control for other systems not shown inFIG. 8, such as controlling input/output devices (e.g., display,keypad), and/or may execute software for one or more applications thatmay be provided on wireless station 800, such as, for example, an emailprogram, audio/video applications, a word processor, a Voice over IPapplication, or other application or software. Moreover, a storagemedium may be provided that includes stored instructions, which whenexecuted by a controller or processor may result in the processor 804,or other controller or processor, performing one or more of thefunctions or tasks described above.

According to another example implementation, RF or wirelesstransceiver(s) 802A/802B may receive signals or data and/or transmit orsend signals or data. Processor 804 (and possibly transceivers802A/802B) may control the RF or wireless transceiver 802A or 802B toreceive, send, broadcast or transmit signals or data.

The aspects are not, however, restricted to the system that is given asan example, but a person skilled in the art may apply the solution toother communication systems. Another example of a suitablecommunications system is the 5G concept. It is assumed that networkarchitecture in 5G will be quite similar to that of the LTE-advanced. 5Gis likely to use multiple input—multiple output (MIMO) antennas, manymore base stations or nodes than the LTE (a so-called small cellconcept), including macro sites operating in co-operation with smallerstations and perhaps also employing a variety of radio technologies forbetter coverage and enhanced data rates. In one example implementation,the smaller station may be a small cell operating at a lower power or ata higher frequency (e.g., above 6GHz). In another exampleimplementation, the smaller station may be a small cell that may be usedas a secondary cell (SCell) for a UE (instead of a primary cell (PCell)or mobility anchor).

It should be appreciated that future networks will most probably utilizenetwork functions virtualization (NFV) which is a network architectureconcept that proposes virtualizing network node functions into “buildingblocks” or entities that may be operationally connected or linkedtogether to provide services. A virtualized network function (VNF) maycomprise one or more virtual machines running computer program codesusing standard or general type servers instead of customized hardware.Cloud computing or data storage may also be utilized. In radiocommunications this may mean node operations may be carried out, atleast partly, in a server, host or node operationally coupled to aremote radio head. It is also possible that node operations will bedistributed among a plurality of servers, nodes or hosts. It should alsobe understood that the distribution of labor between core networkoperations and base station operations may differ from that of the LTEor even be non-existent.

Implementations of the various techniques described herein may beimplemented in digital electronic circuitry, or in computer hardware,firmware, software, or in combinations of them. Implementations may beimplemented as a computer program product, i.e., a computer programtangibly embodied in an information carrier, e.g., in a machine-readablestorage device or in a propagated signal, for execution by, or tocontrol the operation of, a data processing apparatus, e.g., aprogrammable processor, a computer, or multiple computers.Implementations may also be provided on a computer readable medium orcomputer readable storage medium, which may be a non-transitory medium.Implementations of the various techniques may also includeimplementations provided via transitory signals or media, and/orprograms and/or software implementations that are downloadable via theInternet or other network(s), either wired networks and/or wirelessnetworks. In addition, implementations may be provided via machine typecommunications (MTC), and also via an Internet of Things (IOT).

The computer program may be in source code form, object code form, or insome intermediate form, and it may be stored in some sort of carrier,distribution medium, or computer readable medium, which may be anyentity or device capable of carrying the program. Such carriers includea record medium, computer memory, read-only memory, photoelectricaland/or electrical carrier signal, telecommunications signal, andsoftware distribution package, for example. Depending on the processingpower needed, the computer program may be executed in a singleelectronic digital computer or it may be distributed amongst a number ofcomputers.

Furthermore, implementations of the various techniques described hereinmay use a cyber-physical system (CPS) (a system of collaboratingcomputational elements controlling physical entities). CPS may enablethe implementation and exploitation of massive amounts of interconnectedICT devices (sensors, actuators, processors microcontrollers, . . . )embedded in physical objects at different locations. Mobile cyberphysical systems, in which the physical system in question has inherentmobility, are a subcategory of cyber-physical systems. Examples ofmobile physical systems include mobile robotics and electronicstransported by humans or animals. The rise in popularity of smartphoneshas increased interest in the area of mobile cyber-physical systems.Therefore, various implementations of techniques described herein may beprovided via one or more of these technologies.

A computer program, such as the computer program(s) described above, canbe written in any form of programming language, including compiled orinterpreted languages, and can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitor part of it suitable for use in a computing environment. A computerprogram can be deployed to be executed on one computer or on multiplecomputers at one site or distributed across multiple sites andinterconnected by a communication network.

Method steps may be performed by one or more programmable processorsexecuting a computer program or computer program portions to performfunctions by operating on input data and generating output. Method stepsalso may be performed by, and an apparatus may be implemented as,special purpose logic circuitry, e.g., an FPGA (field programmable gatearray) or an ASIC (application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer, chip orchipset. Generally, a processor will receive instructions and data froma read only memory or a random access memory or both. Elements of acomputer may include at least one processor for executing instructionsand one or more memory devices for storing instructions and data.Generally, a computer also may include, or be operatively coupled toreceive data from or transfer data to, or both, one or more mass storagedevices for storing data, e.g., magnetic, magneto optical disks, oroptical disks. Information carriers suitable for embodying computerprogram instructions and data include all forms of non volatile memory,including by way of example semiconductor memory devices, e.g., EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto optical disks; and CD ROM and DVD-ROMdisks. The processor and the memory may be supplemented by, orincorporated in, special purpose logic circuitry.

1-28. (canceled)
 29. A method of communications, comprising: receiving,by a user equipment (UE), a machine learning (ML) configuration from anetwork node, the machine learning (ML) configuration causing the UE tocollect data in a structure being used for machine learning (ML) at thenetwork node; collecting, by the user equipment (UE), machine learning(ML) data based at least on the machine learning (ML) configurationreceived from the network node, the machine learning (ML) data beingcollected from one or more layers of the user equipment (UE) in acoordinated manner; and transmitting, by the user equipment (UE), thecollected machine learning (ML) data to the network node.
 30. The methodof claim 29, further comprising: receiving, by the user equipment (UE),a UECapabilityEnquiry message from the network node; and transmitting,by the user equipment (UE), a UECapabilityInformation message to thenetwork node, the UECapabilityInformation message is transmitted to thenetwork node in response to the receiving of the UECapabilityEnquirymessage from the network node, wherein the machine learning (ML)configuration is received from the network node based at least onmachine learning (ML) capabilities of the UE indicated to the networknode in the UECapabilitylnformation message.
 31. The method of claim 29,wherein the user equipment (UE) coordinates the collecting of themachine learning (ML) data at the user equipment (UE), the collectingbased at least on a machine learning (ML) command in the machinelearning (ML) configuration.
 32. The method of claim 29, wherein themachine learning (ML) configuration includes a machine learning (ML)command that indicates the machine learning (ML) data to be collected inthe coordinated manner at the user equipment (UE).
 33. The method ofclaim 32, wherein the machine learning (ML) command is received viaradio resource control (RRC) signaling.
 34. The method of claim 33,wherein a machine learning (ML) entity at a radio resource control (RRC)layer of the user equipment (UE) collects the machine learning (ML) datagenerated at the radio resource control (RRC) layer of the userequipment (UE).
 35. The method of claim 34, wherein the machine learning(ML) entity at the radio resource control (RRC) layer of the userequipment (UE) manages the transmitting of the data collected at theradio resource control (RRC) layer of the user equipment (UE) to thenetwork node.
 36. The method of claim 33, wherein a machine learning(ML) entity at a radio resource control (RRC) layer of the userequipment (UE) collects data generated at the radio resource control(RRC) layer of the user equipment (UE) and one or more other layers ofthe user equipment (UE).
 37. The method of claim 36, wherein the one ormore other layers include: a packet data convergence protocol (PDCP)layer; a radio link control (RLC) layer; a media access control (MAC)layer; and a physical (PHY) layer.
 38. The method of claim 36, whereinthe machine learning (ML) entity at the radio resource control (RRC)layer of the user equipment (UE) manages the transmitting of the datacollected at the radio resource control (RRC) layer and the one or moreother layers of the user equipment (UE).
 39. The method of claim 32,wherein the machine learning (ML) command is received via machinelearning (ML) layer signaling.
 40. The method of claim 39, wherein amachine learning (ML) layer of the user equipment (UE) collects themachine learning (ML) data generated at one or more other layers of theuser equipment (UE).
 41. The method of claim 40, wherein the one or moreother layers include: a radio resource control (RRC) layer; a packetdata convergence protocol (PDCP) layer; a radio link control (RLC)layer; a media access control (MAC) layer; and a physical (PHY) layer.42. The method of claim 41, wherein the machine learning (ML) entity atthe machine learning (ML) layer of the user equipment (UE) manages thetransmitting of the machine learning (ML) data collected at the machinelearning (ML) layer to the network node.
 43. The method of claim 29,wherein the network node is a gNB.
 44. An apparatus comprising at leastone processor and at least one memory including instructions, whenexecuted by the at least one processor, cause the apparatus to performoperations comprising: receiving a machine learning (ML) configurationfrom a network node, the machine learning (ML) configuration causing theapparatus to collect data in a structure being used for machine learning(ML) at the network node; collecting machine learning (ML) data based atleast on the machine learning (ML) configuration received from thenetwork node, the machine learning (ML) data being collected from one ormore layers of the apparatus in a coordinated manner; and transmittingthe collected machine learning (ML) data to the network node.
 45. Theapparatus of claim 44, wherein the at least one processor and the atleast one memory cause the apparatus to coordinate the collecting of themachine learning (ML) data at the apparatus, the collecting based atleast on a machine learning (ML) command in the machine learning (ML)configuration.
 46. The apparatus of claim 44, wherein the apparatus is auser equipment (UE).
 47. An apparatus comprising at least one processorand at least one memory including instructions, when executed by the atleast one processor, cause the apparatus to perform operationscomprising: transmitting a machine learning (ML) configuration to a userequipment (UE), the machine learning (ML) configuration causing the UEto collect data in a structure being used for machine learning (ML) atthe apparatus; and receiving machine learning (ML) data from the userequipment (UE), the machine learning (ML) data received in response tothe machine learning (ML) configuration transmitted to the userequipment (UE).
 48. The apparatus of claim 47, wherein the apparatus isa network node.